The corrosion behavior of metallic uranium in humid environments is directly related to the safety and lifespan of nuclear facilities, but its microscopic mechanism has long been constrained by the spatiotemporal scale limitations of traditional density functional theory simulations. This paper systematically reviews the research progress of uranium corrosion in pure water, water-oxygen coexisting, and hydrogen environments. It points out that, limited by computational costs, traditional DFT struggles to reproduce the complex networks involving multi-molecule participation, multi-step reactions, and dynamic surface reconstruction during actual corrosion. This “common scale bottleneck” directly leads to controversies and blind spots regarding the core mechanisms in three key environments: the initial dissociation pathway of water molecules in pure water remains unclear; the “catalytic” acceleration effect of water in water-oxygen environments lacks support from microscopic kinetic landscapes; and the dynamic regulation mechanism of local strain on hydriding kinetics in hydrogen corrosion environments remains to be clarified. Combined with the latest theoretical simulation progress in the uranium-hydrogen system, this paper discusses how Machine Learning Interatomic Potentials, as a transformative tool, provides a new paradigm for bridging this scale gap by merging quantum mechanical accuracy with molecular dynamics efficiency. Finally, the paper looks to the future, pointing out that constructing high-fidelity MLIP for U-water and U-H systems to simulate the entire dynamic corrosion process under realistic surface morphologies and variable stress fields will be the key path to clarifying the controversial mechanisms and achieving a fundamental transition in uranium corrosion research from “static observation” to “dynamic prediction.”